def bscoreCumulative(self, figurename, dataname):
"""-----------------------------------------------------------------------------------------
Bokeh plot of cumulative distribution as a line on right hand axis
:param figurename: string, name of figures (stored in self.figure)
:param dataname: string, name of data frame (stored in self.frame)
:return: True
-----------------------------------------------------------------------------------------"""
data = self.frame[dataname]
figure = self.figure[figurename]
source = ColumnDataSource(data)
figure.extra_y_ranges = {"cumulative": Range1d(start=0.0, end=1.0)}
axis2 = LinearAxis(y_range_name="cumulative")
axis2.ticker.num_minor_ticks = 10
figure.add_layout(axis2, 'right')
figure.line(source=source,
x='score',
y='cumulative',
y_range_name='cumulative',
line_width=2,
color='#1122cc')
# shaded box showing 95% level
box = BoxAnnotation(bottom=0.95,
top=1.0,
y_range_name='cumulative',
fill_color='#FFBBBB',
line_width=3,
line_dash='dashed')
figure.add_layout(box)
return True
def make_new_graph(self):
"""
Creates a new set of graphs.
"""
self.active_lines = {}
legend_strings = []
figure = default_figure(self.get_figure_opts())
for y, i in zip(self.key_group, range(len(self.key_group))):
print("make_new_graph(): plotting {}".format(y))
if y == self.indep_var:
print("make_new_graph(): y={} is the indep var".format(y))
continue
print("make_new_graph(): y={} is a dep var".format(y))
# storing the line renderer objects will allow us to update the
# lines without triggering a page redraw
line = figure.line(self.indep_var, y, source=self.source,
color=self.colors[i], line_width=1.5)
self.active_lines[y] = line
legend_strings.append((y, [line]))
l = Legend(legends=legend_strings, location=(0, 0))
figure.add_layout(l, 'right')
return figure
def calibrate(current, reference, figure):
r = scipy.stats.linregress(x=current, y=reference, alternative='two-sided')
a, b = current[0], current[-1]
ca, cb = (
a * r.slope + r.intercept,
b * r.slope + r.intercept,
)
figure.line(
x=[a, b],
y=[ca, cb],
color="red",
)
rps = Label(x=min(current),
y=(max(reference) - min(reference)) * .8 + min(reference),
x_units="data",
y_units="data",
text=f"slope={r.slope}\nintercept={r.intercept}\n"
f"rvalue={r.rvalue}\npvalue={r.pvalue}\n"
f"stderr={r.stderr}\nintercept_stderr={r.intercept_stderr}",
render_mode='css',
border_line_color='black',
border_line_alpha=1.0,
background_fill_color='white',
background_fill_alpha=1.0)
figure.add_layout(rps)
return dict(slope=r.slope, intercept=r.intercept)
def render(**figure_kwargs):
figure = Figure(**figure_kwargs)
figure.add_layout(Legend())
figure.legend.location = "top_left"
figure.legend.click_policy = "hide"
figure.line(legend_label=yfast,
source=onesource,
x='datetime',
y=yfast,
color='red')
figure.line(legend_label=yslow,
source=onesource,
x='datetime',
y=yslow,
color='blue')
figure.segment(legend_label=y1hist,
source=onesource,
x0='datetime',
x1='datetime',
y0=0,
y1=y1hist,
line_width=6,
color='black',
alpha=0.5)
return figure
def plot(self, figure, color_attr=None, history_step=None, solid_color='#0485d1', circle_attr=None,
circle_attr_transform=lambda x: x, circle_line_color='#cb7723', circle_fill_color='#fcb001',
circle_hover_attrs=[], color_attr_bounds=None):
""" To simply plot the network in a solid color, use color_attr=None and history_step=None.
To plot a fixed attribute of each network reach such as redd capacity, set color_attr to the
name of that attribute and history_step=None.
To plot an attribute of the network's history that varies over time, use history_step along
with the name of that attribute.
color_attr_bounds is None to use the min and max values of that variable in the current plot, or
specifiable to use a standard color range across multiple plots"""
lines = [reach.points for reach in self.reaches]
source = ColumnDataSource({'xs': [list(np.array(line).T[0]) for line in lines],
'ys': [list(np.array(line).T[1]) for line in lines],
'line_widths': [0.5 * reach.strahler_order for reach in self.reaches]})
figure.add_layout(Label(x=self.migration_reach.midpoint[0], y=self.migration_reach.midpoint[1]+750,
text='Migration', text_align='center'))
figure.add_layout(Label(x=self.ocean_reach.midpoint[0], y=self.ocean_reach.midpoint[1]+750,
text='Ocean', text_align='center'))
if history_step is not None:
year = 1 + math.floor(history_step / time_settings['WEEKS_PER_YEAR'])
step_within_year = history_step % time_settings['WEEKS_PER_YEAR']
days_into_year = 1 + step_within_year * time_settings['DAYS_PER_WEEK']
date1 = datetime.date.fromordinal(days_into_year).strftime("%b %e")
date2 = datetime.date.fromordinal(days_into_year + 7).strftime("%b %e")
timestring = "Timestep {0} (step {1} of year {2}, {3} - {4})".format(history_step,
1 + step_within_year,
year,
date1,
date2)
figure.add_layout(Label(x=30, y=700, x_units='screen', y_units='screen', text=timestring))
season, season_color = self.season_label(history_step)
figure.add_layout(Label(x=650, y=700, x_units='screen', y_units='screen', text=season, text_color=season_color))
if color_attr is None:
figure.multi_line('xs', 'ys', source=source, line_color=solid_color, line_width='line_widths')
else:
source.add([reach.reach_statistic(color_attr, history_step) for reach in self.reaches], name='color_values')
color_low_value = min(source.data['color_values']) if color_attr_bounds is None else color_attr_bounds[0]
color_high_value = max(source.data['color_values']) if color_attr_bounds is None else color_attr_bounds[1]
mapper = LinearColorMapper(palette='Viridis256', low=color_low_value, high=color_high_value)
figure.multi_line('xs', 'ys', source=source, line_color={'field': 'color_values', 'transform': mapper}, line_width='line_widths')
fmt = NumeralTickFormatter(format='00')
color_bar = ColorBar(color_mapper=mapper, location=(0, 0), title=color_attr, formatter=fmt, label_standoff=7)
figure.add_layout(color_bar, 'right')
if circle_attr is not None:
circle_source = ColumnDataSource({'xs': [reach.midpoint[0] for reach in self.reaches],
'ys': [reach.midpoint[1] for reach in self.reaches]})
circle_source.add([circle_attr_transform(reach.reach_statistic(circle_attr, history_step))
for reach in self.reaches], name='circle_sizes')
for attr in circle_hover_attrs:
circle_source.add([reach.reach_statistic(attr, history_step) for reach in self.reaches], name=attr)
figure.scatter('xs', 'ys', source=circle_source, name='scatterplot', marker='circle', size='circle_sizes',
line_color=circle_line_color, fill_color=circle_fill_color, alpha=0.5)
hover_tooltips = []
for attr in circle_hover_attrs:
hover_tooltips.append((attr, "@"+attr))
figure.add_tools(HoverTool(tooltips=hover_tooltips, names=['scatterplot']))
figure.toolbar.logo = None
def color_bar(figure, color_mapper, **kwargs):
"""
Add a ColorBar to the input figure
Arguments:
figure (bokeh.plotting.GlyphRenderer): figure to add the ColorBar to
color_mapper (bokeh.models.mappers.Mapper): mapper object
kwargs: additional ColorBar properties
"""
figure.add_layout(ColorBar(color_mapper=color_mapper, **kwargs), 'left')
line_color=c_black,
line_width=1,
fill_color=c_orange,
fill_alpha=0.75)
figure.add_glyph(source, visual)
# sigma arrows
source = force_symbol.data_source['sigma_arrows']
visual = Arrow(end=OpenHead(line_color=c_black, line_width=1, size=5),
x_start='xS',
y_start='yS',
x_end='xE',
y_end='yE',
line_color=c_black,
line_width=1,
source=source)
figure.add_layout(visual)
# R arrows
source = force_symbol.data_source['R_arrow']
visual = Arrow(end=OpenHead(line_color=c_black, line_width=2, size=10),
x_start='xS',
y_start='yS',
x_end='xE',
y_end='yE',
line_color=c_black,
line_width=2,
source=source)
figure.add_layout(visual)
# plot labels
source = force_symbol.data_source['R_label']
visual = LatexLabelSet(x='x',
y='y',